Deep Learning Based on NASNet for Plant Disease Recognition Using Leave Images

Adedoja, Adedamola O.; Owolawi, Pius A.; Mapayi, Temitope

doi:10.1109/icabcd.2019.8851029

Cited by 62 publications

(21 citation statements)

References 16 publications

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“…Furthermore, advanced technology is underway to use robots with AI to support cultivation to reduce labor costs. For example, pesticide application 15 and automated harvesting 16,17 .…”

Section: Shape Classification Technology Of Pollinated Tomato Flowers...mentioning

confidence: 99%

Shape classification technology of pollinated tomato flowers for robotic implementation

Hiraguri

Kimura

Endo

et al. 2023

Sci Rep

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Three pollination methods are commonly used in the greenhouse cultivation of tomato. These are pollination using insects, artificial pollination (by manually vibrating flowers), and plant growth regulators. Insect pollination is the preferred natural technique. We propose a new pollination method, using flower classification technology with Artificial Intelligence (AI) administered by drones or robots. To pollinate tomato flowers, drones or robots must recognize and classify flowers that are ready to be pollinated. Therefore, we created an AI image classification system using a machine learning convolutional neural network (CNN). A challenge is to successfully classify flowers while the drone or robot is constantly moving. For example, when the plant is shaking due to wind or vibration caused by the drones or robots. The AI classifier was based on an image analysis algorithm for pollination flower shape. The experiment was performed in a tomato greenhouse and aimed for an accuracy rate of at least 70% for sufficient pollination. The most suitable flower shape was confirmed by the fruiting rate. Tomato fruit with the best shape were formed by this method. Although we targeted tomatoes, the AI image classification technology is adaptable for cultivating other species for a smart agricultural future.

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“…Furthermore, advanced technology is underway to use robots with AI to support cultivation to reduce labor costs. For example, pesticide application 15 and automated harvesting 16,17 .…”

Section: Shape Classification Technology Of Pollinated Tomato Flowers...mentioning

confidence: 99%

Shape classification technology of pollinated tomato flowers for robotic implementation

Hiraguri

Kimura

Endo

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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“…Authors achieved an accuracy of 92.00% . Adedoja et al [12] also worked on transfer learning. They used PlantVillage dataset and NasNet architecture on CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Detection and Diagnosis of Plant Disease Using Deep and Machine Learning Methods

2023

IRJMETS

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In a time of lower crop yields and rampant plant diseases, techniques like deep learning and machine learning aided by developed computer vision help increase the crop yield and classify, prevent as well as cure plant-related diseases. In the literature review section, we have quoted many instances of the CNN model and methods such as transfer learning, apple forest and others for the detection and classification of diseases on different crops with their spectacular results in numeric form. We have relied on quantitative secondary data to bring new datasets with unique results. Besides, we jotted down a brief introduction to some Machine and deep learning models. In the next and most important section, we evaluated the accuracy of these pre-trained learning models and classical machine learning algorithms by adding thousands of images of plants infected with different plant diseases, dividing these images into training and Test sets by 70/30 proportion, and by using metrics of Performance, precision, accuracy, and recall. Before conclusion, the accuracy and loss were determined by applying every image from the test dataset to each iteration.

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“…A NASNet-based deep CNN architecture was used in [45] to identify leaf diseases in plants, and an accuracy rate of 93.82% was achieved. Rice-and maize-leaf diseases were identified by Chen et al [2] using the INC-VGGN method.…”

Section: Deep-learning-based Identificationmentioning

confidence: 99%

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

et al. 2021

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The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

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Deep Learning Based on NASNet for Plant Disease Recognition Using Leave Images

Cited by 62 publications

References 16 publications

Shape classification technology of pollinated tomato flowers for robotic implementation

Shape classification technology of pollinated tomato flowers for robotic implementation

Smart Detection and Diagnosis of Plant Disease Using Deep and Machine Learning Methods

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

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